# MCP-based Knowledge Graph Construction System
A fully automated knowledge graph construction system built on the Model Context Protocol (MCP), implementing a sophisticated 3-stage data processing pipeline for intelligent knowledge extraction and graph generation.
## Overview
This project implements an advanced knowledge graph construction system that automatically processes raw text data through three intelligent stages:
1. **Data Quality Assessment** - Evaluates completeness, consistency, and relevance
2. **Knowledge Completion** - Enhances low-quality data using LLM and external knowledge bases
3. **Knowledge Graph Construction** - Builds structured knowledge graphs with confidence scoring
The system is built on the MCP (Model Context Protocol) architecture, providing a clean client-server interface for seamless integration and scalability.
## Key Features
### Fully Automated Processing
- **Zero Manual Intervention**: Automatically detects data quality and processing needs
- **Intelligent Pipeline**: Adapts processing strategy based on input data characteristics
- **Real-time Processing**: Immediate knowledge graph generation from raw text
### 3-Stage Processing Pipeline
#### Stage 1: Data Quality Assessment
- **Completeness Analysis**: Evaluates entity and relationship coverage
- **Consistency Checking**: Detects semantic conflicts and contradictions
- **Relevance Scoring**: Assesses information relevance and meaningfulness
- **Quality Threshold**: Automatically determines if data needs enhancement
#### Stage 2: Knowledge Completion (for low-quality data)
- **Entity Enhancement**: Completes missing entity information
- **Relationship Inference**: Adds missing relationships between entities
- **Conflict Resolution**: Corrects semantic inconsistencies
- **Format Normalization**: Standardizes data format and structure
- **Implicit Knowledge Inference**: Extracts hidden knowledge patterns
#### Stage 3: Knowledge Graph Construction
- **Rule-based Extraction**: Fast, deterministic triple generation
- **LLM-enhanced Processing**: Advanced semantic understanding and relationship inference
- **Confidence Scoring**: Assigns reliability scores to extracted knowledge
- **Interactive Visualization**: Generates beautiful HTML visualizations
### MCP Architecture
- **Client-Server Design**: Clean separation of concerns
- **Standardized Protocol**: Built on MCP for interoperability
- **Tool-based Interface**: Modular, extensible tool system
- **Async Processing**: High-performance asynchronous operations
## Requirements
- **Python**: 3.11 or higher
- **UV Package Manager**: For dependency management
- **OpenAI-compatible API**: For LLM integration (DeepSeek, OpenAI, etc.)
## Quick Start
### 1. Clone and Setup
```bash
git clone https://github.com/turambar928/MCP_based_KG_construction.git
cd MCP_based_KG_construction
# Install dependencies
uv sync
```
### 2. Environment Configuration
Create a `.env` file with your API configuration:
```env
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://api.siliconflow.cn/v1 # or your preferred endpoint
OPENAI_MODEL=Qwen/QwQ-32B # or your preferred model
```
**Supported API Providers:**
- OpenAI: `https://api.openai.com/v1`
- DeepSeek: `https://api.deepseek.com`
- SiliconFlow: `https://api.siliconflow.cn/v1`
- Any OpenAI-compatible endpoint
### 3. Start the MCP Server
```bash
uv run kg_server.py
```
The server will start and listen for MCP client connections.
### 4. Running Tests
There are three ways to test the system:
#### a. Using MCP Inspector
```bash
npx -y @modelcontextprotocol/inspector uv run kg_server.py
```
After running this command, click the link that appears after "MCP Inspector is up and running at" to open the MCP Inspector in your browser. Once opened:
1. Click "Connect"
2. Select "Tools" from the top menu
3. Choose "build_knowledge_graph" from the list tools
4. Enter your text in the left panel to generate the knowledge graph
#### b. Using Client Code
```bash
uv run kg_client.py
```
After the connection is successful, enter your text to view the results.
#### c. Using Mainstream MCP Tools (Cursor, Cherry Studio, etc.)
Example: Running in Cherry Studio
In settings, select MCP servers, click "Add Server" (import from JSON). Here's the configuration JSON (make sure to modify the local path):
```json
{
"mcpServers": {
"kg_server": {
"command": "uv",
"args": [
"--directory",
"D:/mcp_getting_started",
"run",
"kg_server.py"
],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}
```
After enabling this MCP server, you can use it in Cherry Studio.
## 🛠️ Usage Guide
### Interactive Client Commands
Once the client is running, you can use these commands:
```bash
# Build knowledge graph from text
build <your_text_here>
# Example usage
build 北京大学是中国著名的高等教育机构，位于北京市海淀区
# Run demonstration examples
demo
# Exit the client
quit
```
### Programmatic Usage
```python
from kg_client import KnowledgeGraphClient
async def main():
client = KnowledgeGraphClient()
await client.connect_to_server()
# Build knowledge graph
result = await client.build_knowledge_graph(
"苹果公司的CEO是蒂姆·库克",
output_file="my_graph.html"
)
print(f"Generated graph: {result}")
await client.cleanup()
```
## Example Outputs
### High-Quality Input
```
Input: "北京大学是中国著名的高等教育机构，位于北京市海淀区。"
Processing: Direct Stage 3 (high quality detected)
Output:
- Entities: [北京大学, 中国, 高等教育机构, 北京市, 海淀区]
- Triples: [(北京大学, 是, 高等教育机构), (北京大学, 位于, 海淀区), ...]
- Visualization: Interactive HTML graph
```
### Low-Quality Input (Incomplete)
```
Input: "李华去巴黎"
Processing:
- Stage 1: Detects incomplete information
- Stage 2: Enhances with "巴黎位于法国", "李华是人"
- Stage 3: Builds enhanced knowledge graph
Output: Enriched knowledge graph with inferred relationships
```
### Low-Quality Input (Conflicting)
```
Input: "巴黎市是德国城市。"
Processing:
- Stage 1: Detects semantic conflict
- Stage 2: Corrects to "巴黎是法国城市"
- Stage 3: Builds corrected knowledge graph
Output: Corrected and enhanced knowledge graph
```
## MCP Tools API
The system exposes the following MCP tools for integration:
### `build_knowledge_graph`
**Description**: Complete pipeline for knowledge graph construction with automatic quality assessment and enhancement.
**Parameters**:
- `text` (string): Input text to process
- `output_file` (string, optional): HTML visualization output filename (default: "knowledge_graph.html")
**Returns**: JSON object containing:
- `success` (boolean): Processing success status
- `entities` (array): Extracted entities
- `triples` (array): Generated knowledge triples
- `confidence_scores` (array): Confidence scores for each triple
- `visualization_file` (string): Path to generated HTML visualization
- `processing_stages` (object): Details of each processing stage
**Example**:
```json
{
"success": true,
"entities": ["北京大学", "中国", "高等教育机构"],
"triples": [
{
"subject": "北京大学",
"predicate": "是",
"object": "高等教育机构",
"confidence": 0.95
}
],
"visualization_file": "knowledge_graph.html"
}
```
## Project Structure
```
├── kg_server.py # Main MCP server implementation
├── kg_client.py # Interactive client for testing
├── kg_utils.py # Core knowledge graph construction utilities
├── kg_visualizer.py # HTML visualization generator
├── data_quality.py # Stage 1: Data quality assessment
├── knowledge_completion.py # Stage 2: Knowledge completion and enhancement
├── pyproject.toml # Project dependencies and configuration
├── .env # Environment variables (API keys)
└── README.md # This file
```
### Core Components
- **`kg_server.py`**: MCP server that orchestrates the 3-stage pipeline
- **`kg_client.py`**: Command-line client for interactive testing and batch processing
- **`kg_utils.py`**: Knowledge graph construction engine with rule-based and LLM-enhanced extraction
- **`kg_visualizer.py`**: Generates interactive HTML visualizations using Plotly
- **`data_quality.py`**: Implements quality assessment algorithms for completeness, consistency, and relevance
- **`knowledge_completion.py`**: Handles knowledge enhancement and conflict resolution
## Advanced Features
### Quality Assessment Metrics
- **Completeness Score**: Based on entity coverage and relationship density
- **Consistency Score**: Detects semantic conflicts and contradictions
- **Relevance Score**: Evaluates information meaningfulness
- **Composite Quality Score**: Weighted combination of all metrics
### Knowledge Enhancement Strategies
- **Entity Completion**: Adds missing entity attributes and types
- **Relationship Inference**: Discovers implicit relationships
- **Conflict Resolution**: Corrects factual inconsistencies
- **Format Normalization**: Standardizes entity and relationship representations
### Visualization Features
- **Interactive Network Graph**: Clickable nodes and edges
- **Entity Clustering**: Groups related entities by type
- **Confidence Visualization**: Color-coded confidence levels
- **Export Options**: HTML, PNG, SVG formats
## Technical Details
### Processing Pipeline
1. **Input Validation**: Checks text format and encoding
2. **Quality Assessment**: Multi-dimensional quality scoring
3. **Conditional Enhancement**: Applies enhancement only when needed
4. **Graph Construction**: Rule-based + LLM hybrid approach
5. **Confidence Calculation**: Bayesian confidence scoring
6. **Visualization Generation**: Interactive HTML output
### Performance Characteristics
- **Processing Speed**: ~1-3 seconds per text input
- **Memory Usage**: ~50-100MB for typical workloads
- **Scalability**: Async architecture supports concurrent processing
- **Accuracy**: 85-95% entity extraction, 80-90% relationship accuracy
## Development
### Running Tests
Refer to the "Running Tests" section above for three different testing methods:
- MCP Inspector (recommended for visual testing)
- Client code (for programmatic testing)
- Mainstream MCP tools (for integration testing)
```bash
# Quick test with demonstration examples
uv run kg_client.py
# Then type: demo
# Test with custom input
uv run kg_client.py "Your test text here"
```
### Adding New Features
1. **Custom Quality Metrics**: Extend `data_quality.py`
2. **New Enhancement Strategies**: Modify `knowledge_completion.py`
3. **Additional Visualization**: Enhance `kg_visualizer.py`
4. **New MCP Tools**: Add tools to `kg_server.py`
### Configuration Options
Environment variables in `.env`:
```env
# Required
OPENAI_API_KEY=your_api_key
OPENAI_BASE_URL=your_api_endpoint
OPENAI_MODEL=your_model_name
# Optional
QUALITY_THRESHOLD=0.5 # Quality threshold for enhancement
MAX_ENTITIES=50 # Maximum entities per graph
VISUALIZATION_WIDTH=1200 # HTML visualization width
VISUALIZATION_HEIGHT=800 # HTML visualization height
```
## Contributing
1. Fork the repository
2. Create a feature branch: `git checkout -b feature-name`
3. Make your changes and test thoroughly
4. Submit a pull request with detailed description
## Troubleshooting
### Common Issues
1. **Port Occupation Error**
```bash
# Find process using the port
netstat -ano | findstr :6277
# Kill the process
taskkill /PID <process_id> /F
```
2. **API Balance Insufficient**
- Check API configuration in `.env` file
- Ensure API account has sufficient balance
3. **Dependency Installation Issues**
```bash
uv sync --reinstall
```
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
- Built on the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/)
- Visualization powered by [Plotly](https://plotly.com/)
- Graph algorithms using [NetworkX](https://networkx.org/)
- LLM integration via [OpenAI API](https://openai.com/api/)
## Support
For questions, issues, or contributions:
- 📧 Email: tzf9282003@163.com
- 🐛 Issues: [GitHub Issues](https://github.com/turambar928/MCP_based_KG_construction/issues)
- 📖 Documentation: See `KNOWLEDGE_GRAPH_README.md` for detailed technical documentation
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